scholarly journals Building the Foundation of Robot Explanation Generation Using Behavior Trees

2021 ◽  
Vol 10 (3) ◽  
pp. 1-31
Author(s):  
Zhao Han ◽  
Daniel Giger ◽  
Jordan Allspaw ◽  
Michael S. Lee ◽  
Henny Admoni ◽  
...  
Keyword(s):  
Autonomous Robots ◽  
Free Form ◽  
Static Structure ◽  
Simulation Code ◽  
Causal Information ◽  
Robot Behavior ◽  
Behavior Trees ◽  
Robot Task ◽  
Task Specification ◽  
Explanation Generation

As autonomous robots continue to be deployed near people, robots need to be able to explain their actions. In this article, we focus on organizing and representing complex tasks in a way that makes them readily explainable. Many actions consist of sub-actions, each of which may have several sub-actions of their own, and the robot must be able to represent these complex actions before it can explain them. To generate explanations for robot behavior, we propose using Behavior Trees (BTs), which are a powerful and rich tool for robot task specification and execution. However, for BTs to be used for robot explanations, their free-form, static structure must be adapted. In this work, we add structure to previously free-form BTs by framing them as a set of semantic sets {goal, subgoals, steps, actions} and subsequently build explanation generation algorithms that answer questions seeking causal information about robot behavior. We make BTs less static with an algorithm that inserts a subgoal that satisfies all dependencies. We evaluate our BTs for robot explanation generation in two domains: a kitting task to assemble a gearbox, and a taxi simulation. Code for the behavior trees (in XML) and all the algorithms is available at github.com/uml-robotics/robot-explanation-BTs.

High-level robot task specification

2003 ◽  
Author(s):  
N. Abe ◽  
S. Sako ◽  
S. Tsuji
Keyword(s):  
Robot Task ◽  
Task Specification ◽  
High Level

scholarly journals Multi-Robot Preemptive Task Scheduling with Fault Recovery: A Novel Approach to Automatic Logistics of Smart Factories

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6536
Author(s):  
Vivian Cremer Kalempa ◽  
Luis Piardi ◽  
Marcelo Limeira ◽  
André Schneider de Oliveira
Keyword(s):  
Augmented Reality ◽  
Task Scheduling ◽  
Task Allocation ◽  
Autonomous Robots ◽  
Small Scale ◽  
Fault Recovery ◽  
Novel Approach ◽  
Robot Task ◽  
Smart Factories ◽  
Multi Robot

This paper presents a novel approach for Multi-Robot Task Allocation (MRTA) that introduces priority policies on preemptive task scheduling and considers dependencies between tasks, and tolerates faults. The approach is referred to as Multi-Robot Preemptive Task Scheduling with Fault Recovery (MRPF). It considers the interaction between running processes and their tasks for management at each new event, prioritizing the more relevant tasks without idleness and latency. The benefit of this approach is the optimization of production in smart factories, where autonomous robots are being employed to improve efficiency and increase flexibility. The evaluation of MRPF is performed through experimentation in small-scale warehouse logistics, referred to as Augmented Reality to Enhanced Experimentation in Smart Warehouses (ARENA). An analysis of priority scheduling, task preemption, and fault recovery is presented to show the benefits of the proposed approach.

Synthesis for Robots: Guarantees and Feedback for Robot Behavior

2018 ◽  
Vol 1 (1) ◽  
pp. 211-236 ◽  
Author(s):  
Hadas Kress-Gazit ◽  
Morteza Lahijanian ◽  
Vasumathi Raman
Keyword(s):  
Formal Methods ◽  
Robot Control ◽  
Formal Synthesis ◽  
Complex Tasks ◽  
Current State ◽  
Robot Behavior ◽  
Mathematical Techniques ◽  
Task Specification

Robot control for tasks such as moving around obstacles or grasping objects has advanced significantly in the last few decades. However, controlling robots to perform complex tasks is still accomplished largely by highly trained programmers in a manual, time-consuming, and error-prone process that is typically validated only through extensive testing. Formal methods are mathematical techniques for reasoning about systems, their requirements, and their guarantees. Formal synthesis for robotics refers to frameworks for specifying tasks in a mathematically precise language and automatically transforming these specifications into correct-by-construction robot controllers or into a proof that the task cannot be done. Synthesis allows users to reason about the task specification rather than its implementation, reduces implementation error, and provides behavioral guarantees for the resulting controller. This article reviews the current state of formal synthesis for robotics and surveys the landscape of abstractions, specifications, and synthesis algorithms that enable it.

Swarm Robotic Systems Based on Collective Behavior of Chloroplasts

2017 ◽  
Vol 29 (3) ◽  
pp. 602-612 ◽  
Author(s):  
Satoshi Hoshino ◽  
◽  
Ryo Takisawa ◽  
Yutaka Kodama ◽  
Keyword(s):  
Light Source ◽  
Collective Behavior ◽  
Autonomous Robots ◽  
Cooperative Behavior ◽  
Ground Surface ◽  
Robotic Systems ◽  
Behavioral Models ◽  
System A ◽  
Robot Behavior ◽  
Behavior Models

[abstFig src='/00290003/15.jpg' width='300' text='Swarming chloroplastic robots around light source' ] In this paper, distributed autonomous robots are used to perform area coverage tasks. In order for robots to cover the ground surface of environments, the coordination of a team of robots is a challenge. For this challenge, we present bio-inspired swarm robotic systems. We focus on the collective behavior of chloroplasts toward a light source. On the basis of the mechanism of the chloroplast, we propose robot behavior models that do not use local communication. The emergence of cooperative behavior through the interaction among the swarming robots is a main contribution of this paper. Based on simulation results, the effectiveness of the chloroplastic robots for the coverage task is discussed in terms of flexibility and scalability. Furthermore, the behavioral models are applied to actual mobile robots. Based on the results of experiments, the applicability of the chloroplastic robots to real environments is discussed. As an application of the swarm robotic system, a specific task, sweeping, is given to actual chloroplastic robots.

scholarly journals Open Issues in Evolutionary Robotics

2016 ◽  
Vol 24 (2) ◽  
pp. 205-236 ◽  
Author(s):  
Fernando Silva ◽  
Miguel Duarte ◽  
Luís Correia ◽  
Sancho Moura Oliveira ◽  
Anders Lyhne Christensen
Keyword(s):  
Evolutionary Computation ◽  
Autonomous Robots ◽  
Behavioral Control ◽  
Evolutionary Robotics ◽  
Simulation Based ◽  
Canonical Approach ◽  
Task Specification ◽  
Open Issues

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize controllers for real autonomous robots based only on a task specification. While a number of studies have shown the applicability of evolutionary robotics techniques for the synthesis of behavioral control, researchers have consistently been faced with a number of issues preventing the widespread adoption of evolutionary robotics for engineering purposes. In this article, we review and discuss the open issues in evolutionary robotics. First, we analyze the benefits and challenges of simulation-based evolution and subsequent deployment of controllers versus evolution on real robotic hardware. Second, we discuss specific evolutionary computation issues that have plagued evolutionary robotics: (1) the bootstrap problem, (2) deception, and (3) the role of genomic encoding and genotype-phenotype mapping in the evolution of controllers for complex tasks. Finally, we address the absence of standard research practices in the field. We also discuss promising avenues of research. Our underlying motivation is the reduction of the current gap between evolutionary robotics and mainstream robotics, and the establishment of evolutionary robotics as a canonical approach for the engineering of autonomous robots.

scholarly journals Online Reconfiguration of Distributed Robot Control Systems for Modular Robot Behavior Implementation

2020 ◽  
Vol 100 (3-4) ◽  
pp. 1283-1308
Author(s):  
Malte Wirkus ◽  
Sascha Arnold ◽  
Elmar Berghöfer
Keyword(s):  
Control Systems ◽  
Robot Control ◽  
Autonomous Robots ◽  
High Complexity ◽  
Software Developer ◽  
Robot Controller ◽  
Software Complexity ◽  
Robot Behavior ◽  
Development Approach ◽  
Operational Modes

AbstractThe use of autonomous robots in areas that require executing a broad range of different tasks is currently hampered by the high complexity of the software that adapts the robot controller to different situations the robot would face. Current robot software frameworks facilitate implementing controllers for individual tasks with some variability, however, their possibilities for adapting the controllers at runtime are very limited and don’t scale with the requirements of a highly versatile autonomous robot. With the software presented in this paper, the behavior of robots is implemented modularly by composing individual controllers, between which it is possible to switch freely at runtime, since the required transitions are calculated automatically. Thereby the software developer is relieved of the task to manually implement and maintain the transitions between different operational modes of the robot, what largely reduces software complexity for larger amounts of different robot behaviors. The software is realized by a model-based development approach. We will present the metamodels enabling the modeling of the controllers as well as the runtime architecture for the management of the controllers on distributed computation hardware. Furthermore, this paper introduces an algorithm that calculates the transitions between two controllers. A series of technical experiments verifies the choice of the underlying middleware and the performance of online controller reconfiguration. A further experiment demonstrates the applicability of the approach to real robotics applications.

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